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. 2025 Jun 4;3(8):952–962. doi: 10.1021/envhealth.5c00022

PM2.5 Constituents and Hospitalizations of a Wide Spectrum of Respiratory Diseases: A Nationwide Case-Crossover Study in China

Ziwei Peng †, Yi Guo †, Shuo Jiang †,‡, Yuan Liu †, Fuchao Wang †, Huihuan Luo †, Yixiang Zhu †, Lu Zhou †, Ya Gao †, Hongliang Zhang §, Renjie Chen †, Jianwei Xuan ∥, Cong Liu †,*, Haidong Kan †,⊥,*
PMCID: PMC12362213  PMID: 40837681

Abstract

Few studies have explored the acute effects of fine particulate matter (PM2.5) constituents on respiratory diseases (RDs), particularly across a broad spectrum of RD subtypes. We analyzed the associations between PM2.5 and its five major constituents [organic matter (OM), black carbon (BC), sulfate (SO4 2–), nitrate (NO3 –), and ammonium (NH4 +)] and RDs (10 major categories and 35 specific) based on the hospitalization records from 153 hospitals in 20 provincial distractions from 2013 to 2020. We found that short-term exposure (lag 0–1) to PM2.5 constituents per interquartile range increase was associated with higher hospitalization risks for acute upper respiratory infections, influenza and pneumonia, other acute lower respiratory infections, chronic lower respiratory diseases, other diseases of the pleura, and other diseases of the respiratory system; the effect estimates were 2.45–2.99%, 2.02–2.71%, 2.98–3.62%, 3.06–3.65%, 3.22–4.52%, and 2.23–3.66%, respectively. Among 35 specific RDs, 12 were significantly affected by PM2.5 and its constituents. Individuals aged >60 years were sensitive to PM2.5 constituent exposure. Our individual-level nationwide study provided a more comprehensive perspective on the associations between PM2.5 constituents and various major and specific RDs, highlighting the necessity of prioritizing targeted control strategies for key constituents to effectively mitigate the burden of RDs in China.

Keywords: PM2.5 constituents, respiratory disease hospitalizations, cause-specific, case-crossover, multicenter study

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Introduction

Respiratory diseases (RDs) pose a huge disease burden worldwide. According to the Global Burden of Disease study, nearly 3.72 million people died of chronic obstructive pulmonary disease (COPD) and nearly 2.2 million people died of lower respiratory infections in 2021. The etiology of respiratory diseases is multifactorial, involving bacterial and viral attacks, allergies, occupational exposures, and environmental factors. − Prior studies have indicated that environmental factors are important contributors to the onset and progression of respiratory diseases. − Among the environmental factors, air pollution, particularly fine particulate matter (PM2.5), stands out as a critical pollutant linked to adverse respiratory outcomes. Previous studies have demonstrated a link between PM2.5 exposure and increased hospitalizations and deaths due to RDs. −

PM2.5 is a complex mixture composed primarily of carbonaceous constituents [e.g., organic carbon (OC), elemental carbon (EC)], secondary inorganic aerosols [e.g., sulfate (SO4 2–), nitrate (NO3 –), ammonium (NH4 +)], metals [e.g., zinc, vanadium, lead, nickel], and crustal and biological constituents. These constituents are indicative of various sources and are associated with distinct health impacts. − Compared to the abundance of studies on the respiratory effects of PM2.5, research exploring the associations between its constituents and RDs is relatively limited, with inconsistent identification of key harmful constituents. For example, a case-crossover study conducted in Tokyo reported that exposure to carbon elements was associated with respiratory mortality. Similarly, in China, a nationwide modeling study reported associations between EC and OC and respiratory mortality. However, a time-series study in Greater Houston, US, found that aluminum, cadmium, and potassium were significantly associated with respiratory related emergency department visits. The plausible explanations for these inconsistencies may include differences in study populations, methodologies, and outcomes, along with variations in the sources and distributions of PM2.5 constituents across studies. Furthermore, these studies mainly focused on the specific populations or selected disease end points, leaving the impact of PM2.5 constituents on the full spectrum of RDs among the general population unclear. − In addition, some studies used the time-series analysis, which may lead to ecological fallacy. − Thus, investigations using individual-level data that link PM2.5 and its constituents with hospital admissions across the wide spectrum of RDs are warranted to clarify unresolved issues and support priority-setting in reducing the burden of PM2.5-related RDs.

As China is the largest developing country, it experiences considerable challenges related to ambient air pollution and a high prevalence of RDs. According to the 2022 China Health Statistical Yearbook, the number of hospital discharges for respiratory diseases was over 9.6 million, with a total medical cost of nearly 71 billion Chinese Yuan in 2021, resulting in a heavy disease burden and economic burden. Given these challenges, understanding the impact of PM2.5 constituents on RDs is crucial. Therefore, we conducted a nationwide individual-level case-crossover study in China to investigate the associations between PM2.5 and its constituents and hospital admissions for full-spectrum RDs.

Materials and Methods

Health Data

Daily hospitalization events were sourced from the SuValue database, with detailed descriptions provided in previous publications. , In brief, medical records from hospitals in 20 provincial regions of China were directly extracted, checked for accuracy, and consolidated to form a structured database at the individual level. Data quality was maintained through strict control procedures applied throughout each phase, including standardized data extraction protocols, cross-verification of records, and systematic validation to identify and resolve inconsistencies or errors. For this analysis, we identified initial hospital admissions related to respiratory diseases from 153 hospitals included in the SuValue database, covering the period from 2013 to 2020 (Figure S1). Primary diagnoses were classified using the International Classification of Diseases, 10th version (ICD-10). Ten major categories of RDs were included, as earlier research has demonstrated their associations with air pollutants. ,,, These diseases included acute upper respiratory infections (J00–J06), influenza and pneumonia (J09–J18), other acute lower respiratory infections (J20–J22), other diseases of the upper respiratory tract (J30–J39), chronic lower respiratory diseases (J40–J47), lung diseases due to external agents (J60–J70), other respiratory interstitium diseases (J80–J84), suppurative and necrotic respiratory tract (J85–J86), other diseases of the pleura (J90–J94), and other diseases of the respiratory system (J95–J99). Additionally, we gathered hospitalization data for 35 specific RDs based on each ICD code. To ensure sufficient statistical power, only diseases with over 500 cases during the study period were included. Information on disease classifications and respective ICD codes is provided in Table . Each patient’s admission date, gender, and age were recorded and associated with a unique anonymized identifier. The SuValue database authorized data usage for this analysis, eliminating the need for ethical review or informed consent. The study protocol received formal approval from Fudan University’s School of Public Health Institutional Review Board (IRB No. 2021-04-0889), which granted an exemption from informed consent requirements.

1. Summary on Numbers of Hospitalization Events for Respiratory Diseases during the Study Period (2013-2020).

      sex
 age (years)
 season
respiratory diseases ICD codes counts male female ≤60 >60 cold warm
acute upper respiratory infections J00–J06 311,605 176,651 134,954 293,707 17,898 137,551 174,054
influenza and pneumonia J09–J18 603,266 341,829 261,437 518,581 84,685 336,313 266,953
other acute lower respiratory infections J20–J22 257,010 143,010 114,000 227,244 29,766 144,971 112,039
other diseases of upper respiratory tract J30–J39 80,233 43,760 36,473 70,668 9565 39,618 40,615
chronic lower respiratory diseases J40–J47 362,389 221,683 140,706 109,540 252,849 204,758 157,631
lung diseases due to external agents J60–J70 8217 7147 1070 3011 5206 4461 3756
other respiratory interstitium diseases J80–J84 11,396 6344 5052 6198 5198 6100 5296
suppurative and necrotic lower respiratory tract J85–J86 2416 1846 570 1461 955 1167 1249
other diseases of the pleura J90–J94 23,339 18,136 5203 15,802 7537 11,407 11,932
other diseases of the respiratory system J95–J99 122,845 66,025 56,820 46,033 76,812 67,992 54,853
acute nasopharyngitis J00 6523 3702 2821 6033 490 2987 3536
acute sinusitis J01 2031 1065 966 1780 251 1095 936
acute pharyngitis J02 19,518 10,936 8582 18,563 955 7511 12,007
acute tonsillitis J03 99,915 59,934 39,981 98,959 956 41,817 58,098
acute laryngitis and bronchitis J04 21,723 13,528 8195 20,488 1235 12,088 9635
acute obstructive laryngitis and epiglottitis J05 4190 2603 1587 3653 537 2131 2059
multiple acute upper respiratory tract infections J06 157,705 84,883 72,822 144,231 13,474 69,922 87,783
influenza caused by viruses J10 902 518 384 799 103 639 263
influenza J11 3314 1860 1454 3167 147 2272 1042
streptococcal pneumonia J13 1456 826 630 1327 129 833 623
bacterial pneumonia J15 54,032 28,234 25,798 37,505 16,527 28,620 25,412
pneumonia J18 541,357 309,111 232,246 473,686 67,671 302,775 238,582
acute bronchitis J20 202,724 107,813 94,911 174,728 27,996 109,248 93,476
acute bronchiolitis J21 54,188 35,143 19,045 52,442 1746 35,664 18,524
chronic sinusitis J32 21,170 11,850 9320 17,767 3403 10,968 10,202
nasal polyps J33 3659 2202 1457 2985 674 1962 1697
other disorders of the nose and sinuses J34 12,899 8406 4493 11,685 1214 6602 6297
chronic diseases of tonsils and adenoids J35 13,874 7747 6127 13,604 270 6613 7261
peritonsillar abscess J36 2954 2060 894 2636 318 1383 1571
vocal cord and larynx diseases J38 15,540 6343 9197 13,763 1777 6973 8567
bronchitis, not specified as acute or chronic J40 48,176 25,451 22,725 39,008 9168 26,624 21,552
unspecified chronic bronchitis J42 39,465 22,334 17,131 8690 30,775 22,946 16,519
emphysema J43 10,295 7521 2774 2103 8192 5679 4616
other chronic obstructive pulmonary disease J44 212,079 141,924 70,155 28,231 183,848 122,602 89,477
asthma J45 28,557 13,751 14,806 20,538 8019 14,775 13,782
bronchiectasis J47 23,244 10,396 12,848 10,645 12,599 11,818 11,426
coal worker’s pneumoconiosis J60 2826 2822 4 607 2219 1585 1241
pneumoconiosis caused by silicon dust J62 1638 1604 34 500 1138 911 727
unspecified pneumoconiosis J64 662 656 6 278 384 349 313
other interstitial lung diseases J84 10,652 5834 4818 5668 4984 5742 4910
empyema J86 711 539 172 408 303 350 361
pneumothorax J93 11,908 10,693 1215 9408 2500 5698 6210
other pleural conditions J94 10,655 6946 3709 5866 4789 5343 5312
respiratory failure, not classified elsewhere J96 9528 6036 3492 2003 7525 5396 4132
other respiratory disorders J98 113,160 59,890 53,270 43,978 69,182 62,519 50,641
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Assessment of Exposure

Daily measurements of PM2.5 and its chemical constituents, including PM2.5, organic matter (OM), black carbon (BC), SO4 2–, NO3 –, and NH4 +, were collected from tracking air pollution in China (TAP, http://tapdata.org.cn). Additionally, daily concentrations of ozone (O3, maximum 8 h average) were sourced from the National Air Quality Monitoring System. The TAP database offers an extensive data set of PM2.5 chemical constituents in near-real-time, covering from 2013 to 2020 with a spatial resolution of 10 km. , This data set was developed by combining outputs from the Weather Research and Forecasting-Community Multiscale Air Quality modeling system with ground measurements, a machine learning approach, and integrated PM2.5 data from multiple sources. Detailed cross-validation showed a strong agreement between modeled PM2.5 constituents and empirical data, with correlation coefficients between 0.67 and 0.80 and most normalized mean biases within ±20%. Owing to confidentiality constraints, exposure assessment could be feasibly conducted only at the hospital level. In addition, to control for possible confounding factors in meteorological conditions, we obtained daily temperature and relative humidity data from the European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation, which provides hourly estimates at a spatial resolution of 0.1° × 0.1°.

Statistical Analysis

We employed a time-stratified case-crossover design to evaluate the associations between exposure to PM2.5 and its constituents and hospital admissions for both major categories and specific types of RDs. − For each patient, the hospitalization start date was designed as the case day, while control days were selected from the same month and year that matched the same day of the week. This design enables each patient to serve as their own control, thereby inherently adjusting for individual-level factors that remain stable over the short-term such as socioeconomic status and smoking habits.

Spearman correlation analysis was conducted to evaluate the correlation between PM2.5 and its constituents. Conditional logistic regression models were utilized to investigate the associations. First, we explored the optimal lag patterns for each of the PM2.5 constituents. Single-day lags at 0–3 days and their corresponding moving averages were individually incorporated into the model. The lag period yielding the largest effect estimate and the best model fit was selected as the primary lag structure for further analyses. , Second, consistent with previous studies, we utilized a natural spline function to adjust temperature (lag 0–3 days) and relative humidity (lag 0–3 days), with degrees of freedom (df) of 6 and 3, respectively, to account for potential confounding effects of meteorological variables. , Here is the model presented:

logit(P)=β0+β1x+ns(temperature,df=6)+ns(relative humidity,df=3)+strata(id)

where P indicates the probability of hospitalization due to respiratory diseases, x denotes the PM2.5 and its constituents, β0 is the intercept, β1 is the log odds ratio of hospitalization per unit increase in PM2.5 and its constituents, ns represents the natural cubic spline, and the analysis is stratified by id, where each patient is assigned an anonymous identification number.

Furthermore, we applied a method according to previous research to assess the health impacts of PM2.5 constituents. We developed three models as follows: (1) The single-constituent model evaluated each PM2.5 constituent’s effect on RDs, adjusting for other covariates. (2) The constituent-PM2.5 model referred to an extension of the single-constituent model by including PM2.5 as an additional exposure variable. (3) The constituent-residual model was constructed by regressing the PM2.5 mass concentration on each constituent separately and extracting the residuals, which capture the variability of PM2.5 not explained by the respective constituent. These residuals were then included as covariates in the single-constituent model, transforming it into the constituent-residual model. This approach accounts for the remaining PM2.5 variability while naturally mitigating the collinearity. Among the three models, the single-constituent model served as the primary analysis based on its interpretability and alignment with previous studies. ,− The other two models served to validate the robustness of the primary model.

To illustrate the exposure–response associations between PM2.5 constituents and hospitalizations of RDs, we replaced the linear term of PM2.5 and its constituents in the single-constituent model with a natural spline with 3 degrees of freedom (df). We additionally conducted stratified analyses by considering sex (males, females), age (≤60 years old, >60 years old), and season (warm season, April–September; cold season, October–March), which may influence our results. To compare estimates across strata, we employed a two-sample z-test using the formula outlined below:

z=β1−β2SE12+SE22

In this formula, β1 and β2 represent the regression coefficients (expressed as the natural logarithm of the odds ratio) for each stratum, while SE1 and SE2 are their standard errors, respectively.

To examine the stability of the associations, we conducted several sensitivity analyses. First, we incorporated public holidays as a binary indicator in the single-constituent model to adjust for their potential effects. Second, we included O3 in the single-constituent model to control for potential confounding, applying the same lag pattern (lag 0–1) as that used for PM2.5 and its constituents.

Statistical analyses were conducted using R software (version 4.4.1). The estimated effects were expressed as percentage changes in RDs associated with each interquartile range (IQR) increase in PM2.5 and its constituents, accompanied by their 95% confidence intervals (95% CI). Statistical significance was determined using two-sided tests, with thresholds set at P < 0.05.

Results

Table presents the total number of hospitalizations due to respiratory diseases along with stratified counts by age, sex, and season. During the study period (2013–2020), for large categories, there were 311,605 hospitalizations for acute upper respiratory infections, 603,266 for influenza and pneumonia, and 362,389 for chronic lower respiratory diseases. For specific diseases, pneumonia (N = 541,357, 30.37%) and other chronic obstructive pulmonary disease (N = 212,079, 11.90%) accounted for the largest proportion. There were more males (57.58%) than females in this study, and more RDs occurred in individuals aged 60 or younger (72.49%).

Summary statistics on the average daily concentration of PM2.5 and its constituents and weather variables for all patients during case and control periods are presented in Table . The mean exposure levels of PM2.5, BC, OM, SO4 2–, NO3 –, NH4 +, and O3 on the case day were 50.31, 2.71, 12.73, 9.73, 9.50, 6.89, and 79.60 μg/m3, respectively. All of them were slightly higher than the mean exposure levels of the control days but not with statistical difference. The IQRs of PM2.5, BC, OM, SO4 2–, NO3 –, NH4 +, and O3 were 38.00, 2.08, 9.94, 7.46, 9.23, 6.22, and 60.40 μg/m3, respectively. Figure S2 illustrates the results of the Spearman correlation analysis of PM2.5 and its constituents. Moderate to high correlations were observed between PM2.5 and its constituents, with the Spearman correlation (r s) ranging from 0.68 to 0.97.

2. Summary Statistics on Average Daily Concentration of PM2.5 and Its Constituents and Meteorological Conditions for All Patients on Case and Control Days .

      percentiles
  
variable status mean ± SD 25th 50th 75th IQR
PM2.5 (μg/m3) case 50.31 ± 34.81 26.00 41.00 64.00 38.00
control 49.82 ± 34.81 26.00 40.00 63.00 37.00
BC (μg/m3) case 2.71 ± 2.04 1.35 2.13 3.43 2.08
control 2.69 ± 2.04 1.34 2.11 3.40 2.06
OM (μg/m3) case 12.73 ± 9.55 6.30 10.00 16.24 9.94
control 12.60 ± 9.55 6.23 9.88 16.02 9.79
SO4 2– (μg/m3) case 9.73 ± 6.78 5.02 7.89 12.48 7.46
control 9.64 ± 6.78 4.98 7.81 12.35 7.37
NO3 – (μg/m3) case 9.50 ± 8.68 3.45 6.69 12.68 9.23
control 9.41 ± 8.68 3.41 6.62 12.54 9.13
NH4 + (μg/m3) case 6.89 ± 5.79 2.84 5.16 9.06 6.22
control 6.82 ± 5.79 2.81 5.11 8.97 6.16
O3 (μg/m3) case 79.60 ± 43.8 46.60 74.40 107.00 60.40
control 79.50 ± 43.8 46.60 74.40 107.00 60.40
temperature (°C) case 15.68 ± 10.53 8.80 17.20 24.20 15.40
control 15.71 ± 10.53 8.90 17.20 24.30 15.40
relative humidity (%) case 72.44 ± 17.02 63.00 76.00 85.00 22.00
control 72.51 ± 17.02 63.00 76.00 85.00 22.00
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a

Abbreviations: IQR, interquartile range; SD, standard deviation; PM2.5, particulate matter with an aerodynamic diameter less than or equal to 2.5 μm; OM, organic matter; BC, black carbon; SO4 2–, sulfate; NO3 –, nitrate; NH4 +, ammonium.

Figure S3 presents the percentage changes in hospitalization of ten major categories and 35 specific RDs associated with an IQR increase in PM2.5 and its constituents on different lag days. Although the lag patterns varied by health outcome, consistent temporal trends were found for each specific outcome across PM2.5 and its constituents. In general, effect estimates declined from lag day 0 to day 3. The strongest associations, accompanied by the narrowest confidence intervals, were typically identified at the lag of 0–1 day moving average. Therefore, lag 0–1 day was selected as the primary exposure window for all pollutants and outcomes.

Figure illustrates the percentage variations in hospital admissions for ten major categories of RDs per IQR increase in PM2.5 and its constituents on lag 0–1 day. First, short-term exposure to PM2.5 and its constituents significantly elevated hospitalization risks for six major RDs, with estimates for PM2.5 ranging from 2.79% to 4.86%. Among the five major constituents, the risk varied by disease category; for acute upper respiratory infections, with each IQR increase, SO4 2– showed the largest effect [2.99% (95% CI: 2.28%, 3.71%)], followed by OM [2.70% (1.99%, 3.41%)] and NO3 – [2.64% (1.82%, 3.47%)], while chronic lower respiratory diseases showed the same pattern, where the effect estimates of SO4 2–, OM, and NO3 – were 3.65% (3.02%, 4.29%), 3.53% (2.92%, 4.14%), and 3.42% (2.76%, 4.09%), respectively. For influenza and pneumonia, SO4 2– [2.71% (2.21%, 3.21%)] also showed the highest effect, followed by OM [2.48% (2.00%, 2.96%)] and BC [2.37% (1.90%, 2.84%)]. For other acute lower respiratory infections, SO4 2– [3.62% (2.84%, 4.40%)], NH4 + [3.18% (2.37%, 4.00%)], and BC [3.13% (2.40%, 3.86%)] were the top three contributors. Additionally, the effect estimates for other diseases of the pleura ranged from 3.22% to 4.52%, while those other diseases of the respiratory system ranged from 2.23% to 3.66%. Other respiratory interstitium diseases were only significantly associated with BC, OM and SO4 2–, and the corresponding percentage changes in hospitalizations were 3.76% (0.39%, 7.26%), 4.24% (0.67%, 7.94%), and 4.07% (0.33%, 7.95%), respectively. Second, as for specific diseases (Figure S4), PM2.5 and its constituents had similar positive associations with 12 of them. Short-term exposure to PM2.5 and its constituents was significantly associated with several common RDs, such as influenza, asthma, and COPD (ICD-10 code: J44). For COPD, which poses a heavy disease burden in China, PM2.5, BC, OM, SO4 2–, NO3 –, and NH4 + increased the risk of hospitalization by 4.40% (3.56%, 5.25%), 3.51% (2.74%, 4.28%), 4.06% (3.26%, 4.85%), 3.94% (3.12%, 4.77%), 3.64% (2.77%, 4.51%), and 3.12% (2.29%, 3.95%), respectively. For asthma, PM2.5, BC, OM, SO4 2–, NO3 –, and NH4 + increased the risk of hospitalization 2.76% (0.31%, 5.28%), 2.21% (0.01%, 4.46%), 2.52% (0.20%, 4.89%), 2.90% (0.56%, 5.29%), 2.47% (−0.12%, 5.13%), and 2.79% (0.28%, 5.37%). Moreover, PM2.5 constituents also had adverse effects on emphysema, pneumothorax, other pleural conditions, and other relatively rare RDs. Third, among both major categories and specific RDs, SO4 2– and OM consistently exhibited the largest effect estimates and the most robust associations with a wide range of RDs.

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Percent changes in hospitalization for ten major categories of respiratory diseases associated with an IQR increase in PM2.5 and its constituents on lag 0–1 day. Abbreviations: PM2.5, particulate matter with an aerodynamic diameter less than or equal to 2.5 μm; OM, organic matter; BC, black carbon; SO4 2–, sulfate; NO3 –, nitrate; NH4 +, ammonium; The boxes indicate the mean effect estimates, and the bars indicate the upper and lower 95% confidence intervals.

Figure displays the percent changes in the hospitalization of RDs per IQR increase in PM2.5 and its constituents in the single-constituent model, constituent-PM2.5 model, and constituent-residual model on lag 0–1 day. The comparison showed that most of the effect estimates in the constituent-PM2.5 model were lower compared to the main model (single-constituent model), while the results of the constituent-residual model aligned closely with those of the main model, suggesting that the results of the main model were robust.

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Percent changes in hospitalization for major categories of respiratory diseases associated with an IQR increase in PM2.5 and its constituents in the single-constituent model, constituent-PM2.5 model, and constituent-residual model on lag 0–1 day. The single-constituent model evaluated each PM2.5 constituent’s effect on respiratory diseases (RDs), adjusting for meteorological factors. The constituent-PM2.5 model added PM2.5 to the single-constituent model; the constituent-residual model extracted the residuals of each PM2.5 constituent to serve as an alternative metric.

Figure shows the concentration–response relationship curves of ten major categories of RDs associated with PM2.5 and its constituents on lag 0–1 day. Most of the curves monotonically increased and showed broader confidence intervals at higher exposure levels, likely reflecting fewer data points in those ranges. The curves for acute upper respiratory infections, influenza and pneumonia, other acute lower respiratory infections, chronic lower respiratory diseases, and other respiratory interstitium diseases showed larger slopes in the lower concentration range. Additionally, the curves of other diseases of the upper respiratory tract, lung diseases due to external agents, and other diseases of the pleura remained relatively stable at low concentrations and demonstrated sharper increases at higher exposure levels. We also visualized the concentration–response curves for 35 specific RDs with each pollutant (Figure S5). Similarly, most curves exhibited a monotonically increasing trend, and the best fitted curves included pneumonia, acute bronchitis, and other chronic obstructive pulmonary disease.

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Concentration–response relationship curves for major categories of respiratory diseases associated with PM2.5 and its constituents on lag 0–1 day. The solid lines indicate the mean effect estimates, and the gray shades indicate the 95% confidence intervals.

In stratified analysis (Figure S6), although stronger associations were observed between PM2.5 and its constituents and 10 major categories of RDs during the cold seasons than in the warm seasons, the differences lacked statistical significance (P > 0.05). Associations demonstrated variability according to age and sex characteristics. In terms of sex, for acute upper respiratory infections and chronic lower respiratory diseases, the effect estimates of females were generally larger than those of males; for influenza and pneumonia, other acute lower respiratory infections, other diseases of the pleura, and other diseases of the respiratory system, the results were the opposite. However, significant differences were only found in suppurative and necrotic lower respiratory tract only on NH4 + and NO3 – (P < 0.05). In terms of age, for influenza and pneumonia (PM2.5 and its five constituents), other diseases of upper respiratory tract (PM2.5, SO4 2–, NO3 –, NH4 +), lung diseases due to external agents (OM), and other diseases of the pleura (BC, OM), the estimate effects were significantly higher in subgroups with older ages (>60 years) (P < 0.05).

The associations continued to demonstrate statistical significance after controlling for public holidays (Table S1). In the two-pollutant models (Table S2), although most effect estimates slightly decreased, the associations remained robust, except for other diseases of the pleura.

Discussion

To the best of our knowledge, this is the first nationwide case-crossover study in China exploring the effects of PM2.5 and its constituents on daily hospitalization events of full-spectrum RDs. Our study revealed that PM2.5 and its constituents significantly elevated the risks of hospitalization for six major categories of RDs (e.g., acute upper respiratory infections, influenza and pneumonia, and other acute lower respiratory infections) and 12 specific RDs. The effects of PM2.5 and its constituents remained robust across multiple validation models. We also observed positive concentration–response relationships between the PM2.5 constituents and most RDs. Additionally, the elderly are vulnerable to exposure PM2.5 and its constituents. Our findings may offer valuable insights into the health effects of PM2.5 constituents on RDs and provide scientific evidence for future toxicological or mechanistic studies.

This multicenter research revealed that short-term exposure to PM2.5 and its constituents was significantly associated with an increased risk of hospitalization from acute upper respiratory infections, influenza and pneumonia, other acute lower respiratory infections, and other major categories of RDs. However, the results of previous studies on the association of PM2.5 constituents with RDs were inconsistent. For example, Zhang et al. found that a per IQR increase in exposure to PM2.5 (44.6 μg/m3), EC (1.9 μg/m3), OC (5.3 μg/m3), SO4 2– (11.4 μg/m3), NO3 – (15.3 μg/m3), and NH4 + (8.0 μg/m3) was related to an increase in respiratory admissions of 0.8% (95% CI: 0.0%, 1.7%), 1.0% (0.1%, 2.0%), 1.2% (0.2%, 2.2%), 1.0% (0.2%, 1.9%), 1.2% (−0.1%, 2.5%), and 0.8% (0.0%, 1.7%), respectively, in five cities of Hubei province. Basagaña et al. observed increased percent changes for EC [2.6% (0.1%, 5.2%)] for respiratory admissions in five South-European cities, but they did not observe the harmful effects of total carbon, OC, SO4 2–, and NO3 –, and when the constituent residual method was used, the adverse effects of EC also disappeared. A study conducted in the five-county Denver metropolitan area similarly did not find an association between exposure to PM2.5 constituents and an increased risk of respiratory hospital admissions. The various findings across studies may be influenced by differences in population, research methodology, lag structure, corrected covariates in the model, or pollutant levels. In addition, the above studies have generally assessed the hospitalization risk of total respiratory diseases; sometimes this may mask the impact of PM2.5 constituents on the major categories of RDs and make it difficult to detect specific health damage from PM2.5 constituents. Our study had this advantage of health outcome segmentation and may facilitate a more systematic assessment of the effect of PM2.5 constituents on the hospitalization risks across various types of RDs.

For the effect of specific RDs, we observed positive associations between PM2.5 constituents and hospitalization for several common RDs, such as pneumonia, asthma, and COPD, although the significant links of constituents may differ. For example, Hopke et al. observed secondary sulfate and spark-ignition may be the adverse constituents for COPD hospitalizations in New York State, but Zhang et al. found that EC, OC and NH4 + impacted the COPD hospitalizations in China. Regional disparities in results may stem from differences in PM2.5 constituents, alongside population characteristics and socioeconomic factors. For asthma, our results suggested that BC, OM, SO4 2–, and NH4 + were associated with asthma-related hospitalizations, confirming the previous reported effects of carbonaceous constituents on asthma. ,,− However, unlike previous studies that reported harmful effects of NO3 –, we found no significant impact, possibly due to differences in exposure assessment or study population characteristics. , In addition, we discovered the hospitalization risk of rare RDs could be affected by PM2.5 constituents, including emphysema, pneumothorax, other pleural conditions, etc. These risks may be mechanistically linked to the inflammatory and oxidative stress response triggered by PM2.5 and its constituents, which exacerbates underlying respiratory conditions by damaging lung tissue and impairing pulmonary function. − Nevertheless, there was a lack of previous reports on these end points, and more studies with high evidence-based ratings are needed to validate these results in the future.

Notably, our study observed larger effects of OM and SO4 2– over other constituents. Previous studies indicated that OM primarily originates from transportation emissions, industrial pollution, and biomass burning, while SO4 2– is mainly derived from coal combustion. These results underscore the need for air pollution control strategies to prioritize emission reductions from these key sources. Furthermore, our analysis found that BC, NO3 –, and NH4 + also have harmful effects on the respiratory system. BC is mainly produced by the incomplete combustion of fossil fuels and biomass. NO3 – is primarily derived from traffic pollution and secondary pollution, whereas NH4 + largely originates from agricultural activities, with a smaller contribution from transportation. Therefore, adopting targeted measures to mitigate emissions from these sources is of equal importance. From a mechanistic perspective, the observed health effects may be attributed to the physicochemical properties of these constituents. For example, Song et al. reported that the presence of SO4 2– increases the acidity of the PM2.5, further enhancing the solubility of the metal fractions, leading to an increase in the cytotoxicity of the PM2.5. As for OM (approximate to OC), it is metabolizable in the organism into electrophilic reactive metabolites that induce the production or increase of intracellular reactive oxygen species (ROS). Furthermore, some OM (e.g., polycyclic aromatic hydrocarbon, n-alkanes) may alter the immune system, further leading to a decrease in the body’s resistance and exacerbating symptoms of respiratory diseases. Future studies focusing on mechanistic evidence are essential to further validate these findings. Additionally, a more comprehensive joint analysis of PM2.5 constituents is needed to explore their combined effects on health outcomes.

Apart from the primary single-constituent models, we observed that while the constituent-PM2.5 model results were less stable, likely due to high collinearity, the constituent-residual model demonstrated more robust and consistent findings. The latter model showed that variability attributed to the remaining PM2.5 mass is accounted for while naturally avoiding collinearity, ensuring the reliability and validity of our main findings. Remarkably, the exposure–response relationship curves revealed distinct patterns across different respiratory diseases. For acute upper respiratory infections, influenza and pneumonia, other acute lower respiratory infections, chronic lower respiratory diseases, and other respiratory interstitium diseases, the steeper slopes observed at lower concentration ranges suggest that continuously reducing the concentration of PM2.5 and its constituents will bring substantial health benefits. For some other diseases, such as other diseases of the upper respiratory tract, lung diseases due to external agents, and other diseases of the pleura, the curves exhibited steeper slopes at higher exposure levels, highlighting the importance of targeted interventions in areas with severe air pollution to mitigate these specific health risks.

Our study used stratified analyses to identify potential effect modifiers affecting the associations between PM2.5 constituents and RDs, revealing some heterogeneity in the effect estimates among different strata. First, in terms of age, our findings aligned with previous studies, consistently demonstrating that elderly individuals are particularly vulnerable to air pollution-related health risks. ,, Specifically, for influenza and pneumonia, we observed that the effect estimates for individuals over 60 years old were nearly twice as high as those for younger individuals. This disparity appears to be primarily driven by pneumonia, possibly explained by the decline in physical performance and immunological efficiency that occurs with age. These findings underscore the critical need to reduce ambient concentrations of PM2.5 and its constituents to protect susceptible populations and mitigate the burden of disease. For other respiratory conditions, such as other diseases of the upper respiratory tract, lung diseases due to external agents, and other diseases of the pleura, differences between elderly and younger individuals were pronounced. However, most effect estimates were nonsignificant or had wide confidence intervals, indicating limited evidence of differential susceptibility. Second, regarding sex differences, although previous studies reported females to be more susceptible to specific pollutants (e.g., EC, NO3 – and NH4 +), ,, our study did not identify consistent modifying effects of sex across all respiratory end points. This could be due to our investigation through a wide spectrum of diseases, and sex might have a different effect on various respiratory end points. Third, in terms of seasonal variations, our results indicated that effect estimates tended to be greater in the cold season compared to those in the warm season, although the variations did not achieve statistical significance. This pattern aligns with previous studies and may be explained by several factors, , including increased air pollution exposure due to heating activities, higher rates of respiratory infections during colder months, and the exacerbation of respiratory symptoms by cold air, particularly among individuals with pre-existing conditions such as COPD or asthma. −

Our results may have some implications for accurately controlling PM2.5 pollution and reducing the disease burden. First, we utilized a nationally representative, multicenter, individual-level data analysis approach to provide evidence for respiratory disease control strategies. Furthermore, the high-precision, comprehensive PM2.5 constituent data helped reduce exposure bias and established a foundation for accurate identification of health risks. Lastly, the findings from our stratified analysis underscored the necessity of paying attention to a susceptible population such as the elderly, which will bring significant health benefits.

We acknowledge that several limitations existed for the current study. First, the cases in the study were based on preliminary diagnostic definitions, diagnostic and clinical classification, or coding errors in RDs, as well as potential selection bias, which is unavoidable in a national study. However, these errors usually occur randomly, and their effect on the results may be attenuated by the large sample size. Second, exposure matching was performed at the hospital level, which may lead to exposure misclassification. Nevertheless, since patients usually choose to visit the nearest hospital, the impact of hospital-level exposure-based matching on the results should be relatively small. Third, we could not completely rule out the interference of risk factors that change over time (e.g., diet patterns, sleep conditions, exercise frequency), although the probability of these variables changing significantly in the short term is relatively low, limiting their impact on the results. Fourth, the small number of admissions for some of the RDs that could not be included in this study somewhat constrained our ability to investigate these diseases in depth. Fifth, due to the data limitation, we were unable to include other PM2.5 constituents (e.g., metals, dust), which restricted our ability to explore their effects on respiratory hospitalizations and potential interactions among the constituents.

In summary, in this nationwide case-crossover study, we found that short-term exposure to PM2.5 and its constituents significantly increased the risk of hospitalization for six categories of RDs and 12 specific RDs, with the largest effect estimates occurring universally in OM and SO4 2–. Exposure–response relationship curves mostly increased monotonically. Our study systematically explored the effects of PM2.5 constituent exposure on hospitalization across a full spectrum of RDs. It offers key evidence for reducing the RDs burden by addressing the sources PM2.5 constituents and provides essential data for precise air pollution control policies in China.

Supplementary Material

eh5c00022_si_001.pdf (12.4MB, pdf)

Acknowledgments

This work was supported by the National Natural Science Foundation of China (82422065), the National Key Research and Development Program of China (2022YFC2704604), the Shanghai 3-year Public Health Action Plan (GWVI-11.2-YQ32, GWVI-11.1-39, GWVI-11.2-YQ31), and the Shanghai B&R Joint Laboratory Project (22230750300).

The individual data that underlie the results reported in this article, the study protocol, and the statistical analysis plan will be made available to individual researchers on reasonable request by contacting the corresponding author.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00022.

  • Locations of hospitals included in the study, Spearman correlation coefficients between the PM2.5 and its constituents, percent changes in hospitalization of respiratory diseases associated with an IQR increase in PM2.5 and its constituents, and concentration–response relationship curves of specific respiratory diseases associated with PM2.5 and its constituents (PDF)

∇.

Ziwei Peng, Yi Guo, and Shuo Jiang contributed equally to this work. Haidong Kan and Cong Liu are the joint corresponding authors and contributed to the conceptualization, funding acquisition, project administration and supervision. Ziwei Peng, Yi Guo, and Shuo Jiang are the joint first authors and contributed equally to data curation, investigation, methodology, formal analysis, and writing–original draft. Hongliang Zhang, Renjie Chen, and Jianwei Xuan contributed to methodology, software, and validation. Yuan Liu, Fuchao Wang, Huihuan Luo, Yixiang Zhu, Lu Zhou, and Ya Gao contributed to data curation and investigation. All authors contributed to writing–review and editing.

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

eh5c00022_si_001.pdf (12.4MB, pdf)

Data Availability Statement

The individual data that underlie the results reported in this article, the study protocol, and the statistical analysis plan will be made available to individual researchers on reasonable request by contacting the corresponding author.

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